143 lines
5.4 KiB
HTML
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143 lines
5.4 KiB
HTML
Executable file
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<a href="Scientific-module.html">Package Scientific</a> ::
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<a href="Scientific.Clustering-module.html">Package Clustering</a> ::
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Module AffinityPropagation
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<!-- ==================== MODULE DESCRIPTION ==================== -->
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<h1 class="epydoc">Module AffinityPropagation</h1><p class="nomargin-top"></p>
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<p>Clustering by Affinity Propagation</p>
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<p>This clustering algorithm identifies clusters in a set of data items
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based on a list of similarities between the items. The result is a list
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of clusters, each cluster being defined by one 'exemplar' (the item that
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is most representative of the cluster) and by other items. The number of
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clusters is not specified in advance. Instead, a parameter called
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'preference' indicates how likely each item is to be an exemplar. Often
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it is set to the same value for all items. Low preference values yield
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few big clusters, whereas high preference values yield many small
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clusters.</p>
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<p>The algorithm is described in: B.J. Frey & D. Dueck, Science 315,
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972-976 (2007)</p>
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A collection of data items with similarities
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